Glitch power analysis and optimization engine

ABSTRACT

A switching activity report of simulated switching activities of a semiconductor circuit is accessed. A plurality of glitch bottleneck ratios corresponding to a plurality of pins in the semiconductor circuit are determined, comprising by: setting an initial bottleneck ratio on a leaf output pin; and backward traversing the semiconductor circuit to determine a plurality of glitch bottleneck ratios of pins in a fan-in cone of the leaf output pin. 
     A plurality of total glitch powers associated with the plurality of pins is determined, a total glitch power of the plurality of total glitch powers being determined based on a glitch bottleneck ratio and a glitch power of a corresponding pin. One or more critical bottleneck pins among the plurality of pins are identified based on the plurality of total glitch powers. One or more gates associated with the one or more critical bottleneck pins are adjusted to reduce corresponding one or more total glitch powers of the one or more gates.

CROSS REFERENCE TO OTHER APPLICATIONS

This application claims priority to U.S. Provisional Patent ApplicationNo. 63/034,189 entitled GLITCH POWER ANALYSIS AND OPTIMIZATION ENGINEfiled Jun. 3, 2020 which is incorporated herein by reference for allpurposes.

BACKGROUND OF THE INVENTION

The advent of FinFET (Fin Field-Effect Transistor) technology hasgreatly reduced circuit leakage power as an improvement. Circuit totalpower consumption is thus more driven by “dynamic power”, where isreferred to herein as the power consumed while the circuit componentslike logic gates are active, for example during a rise or falltransition. It would be useful to focus analysis and optimization onreducing dynamic power during circuit design and implementation to, forexample, improve battery life, reduce heat and/or thermal noise, improvepower efficiency, reduce power requirements, and reduce weight/size ofproducts associated with said circuit.

BRIEF DESCRIPTION OF THE DRAWINGS

Various embodiments of the invention are disclosed in the followingdetailed description and the accompanying drawings.

FIG. 1 is a functional diagram illustrating a programmed computer/serversystem for glitch power analysis and/or optimization in accordance withsome embodiments.

FIG. 2 is an illustration of a generated glitch example.

FIG. 3 is an illustration of a glitch bottleneck example.

FIG. 4 is an illustration of a glitch power bottleneck calculation usinga statistical glitch power analysis.

FIG. 5 is a flow chart illustrating an embodiment of a process fordynamic power analysis.

FIG. 6 is an illustration of a uniform distribution model to determine agenerated glitch rate.

FIG. 7 is an illustration of a uniform distribution model surfaceintegration analysis.

FIG. 8 is a flow chart illustrating an embodiment of a process forglitch power optimization.

FIG. 9 is an illustration of optimization techniques to reduce generatedglitch power.

FIG. 10A is a flow chart illustrating an embodiment of a process forglitch power analysis.

FIG. 10B is a flow chart illustrating an embodiment of a process fordetermining glitch bottleneck ratios corresponding to pins.

DETAILED DESCRIPTION

The invention can be implemented in numerous ways, including as aprocess; an apparatus; a system; a composition of matter; a computerprogram product embodied on a computer readable storage medium; and/or aprocessor, such as a processor configured to execute instructions storedon and/or provided by a memory coupled to the processor. In thisspecification, these implementations, or any other form that theinvention may take, may be referred to as techniques. In general, theorder of the steps of disclosed processes may be altered within thescope of the invention. Unless stated otherwise, a component such as aprocessor or a memory described as being configured to perform a taskmay be implemented as a general component that is temporarily configuredto perform the task at a given time or a specific component that ismanufactured to perform the task. As used herein, the term ‘processor’refers to one or more devices, circuits, and/or processing coresconfigured to process data, such as computer program instructions.

A detailed description of one or more embodiments of the invention isprovided below along with accompanying figures that illustrate theprinciples of the invention. The invention is described in connectionwith such embodiments, but the invention is not limited to anyembodiment. The scope of the invention is limited only by the claims andthe invention encompasses numerous alternatives, modifications andequivalents. Numerous specific details are set forth in the followingdescription in order to provide a thorough understanding of theinvention. These details are provided for the purpose of example and theinvention may be practiced according to the claims without some or allof these specific details. For the purpose of clarity, technicalmaterial that is known in the technical fields related to the inventionhas not been described in detail so that the invention is notunnecessarily obscured.

To achieve low power design, a design and implementation tool such as aplace and route (P&R) tool may consider dynamic power during itsoptimization flow. As logic gates and interconnect wire have non-zerodelay, logic gates may have multiple toggles before they reach steadylogic state in each clock cycle. The dynamic power triggered by thesenon-functional toggles are referred to herein as “glitch power”. Usinganalysis of glitch power to optimize circuit design and implementationis disclosed.

Circuit glitch power varies with input patterns. To get accurate toggleinformation for all the logic gates in a circuit, typically millions ofsimulation cycles using different input patterns are needed foranalysis, for example using a dynamic simulation, and saved to a file,for example a VCD (Value Change Dump) file. A VCD is an ASCII-basedformat for dumpfiles generated by design tools, and is defined in IEEEStandard 1364-1995 and IEEE Standard 1364-2001. In alternateembodiments, a FSDB (Fast Signal Database), WLF (Wave Log File), SHM(Stimulus File), VPD (binary value dump), SAIF (Switching ActivityInterface Format) file, or any signal/switching activity report may beused without limitation. A signoff power analysis tool may read in a VCDfile or other file to do glitch power analysis

Due to this long simulation time, this type of traditional glitch poweranalysis is time consuming. Furthermore, traditionally there has been noincremental update capability; that is, if a designer changes any partof the design, they have to repeat the entire the process to find a newglitch power value of the design. Because of the constant changes in animplementation flow like a P&R flow, traditional glitch power analysisis impractical for these flows and so traditional circuits designed bysuch tools are not dynamic power optimized. A signoff glitch poweranalysis flow may include:

-   -   dynamic gate-level simulation, for example VCS or NCSIM, to        generate zero and non-zero delay SAIF files;    -   using a single non-zero delay VCD file is sufficient to extract        a glitch toggle rate per instance/gate; and/or    -   a power analysis tool reads the VCD file, SAIF file, or other        signal/switching report to perform dynamic, functional, and/or        glitch power analysis.

Another traditional approach is a statistical approach for quick glitchpower analysis. While the traditional approach does not require atime-consuming dynamic simulation, this statistical approach does notconsider logic correlation in the circuit, and results may be differentfrom a real glitch power value. Using such a model directly in animplementation/P&R flow, while practical in speed, may produce randomand/or unpredictable glitch power in the final design which does notoptimize well or may even be counterproductive. A statistical glitchpower estimation flow may be less accurate but fast, reveal physicalinformation on glitch power reduction techniques, and easier to adoptinto implementation/P&R flows.

FIG. 1 is a functional diagram illustrating a programmed computer/serversystem for glitch power analysis and/or optimization in accordance withsome embodiments. As shown, FIG. 1 provides a functional diagram of ageneral purpose computer system programmed to provide glitch poweranalysis and/or optimization in accordance with some embodiments. Aswill be apparent, other computer system architectures and configurationsmay be used for glitch power analysis and/or optimization.

Computer system 100, which includes various subsystems as describedbelow, includes at least one microprocessor subsystem, also referred toas a processor or a central processing unit (“CPU”) (102). For example,processor (102) can be implemented by a single-chip processor or bymultiple cores and/or processors. In some embodiments, processor (102)is a general purpose digital processor that controls the operation ofthe computer system 100. Using instructions retrieved from memory (110),the processor (102) controls the reception and manipulation of inputdata, and the output and display of data on output devices, for exampledisplay and graphics processing unit (GPU) (118).

Processor (102) is coupled bi-directionally with memory (110), which caninclude a first primary storage, typically a random-access memory(“RAM”), and a second primary storage area, typically a read-only memory(“ROM”). As is well known in the art, primary storage can be used as ageneral storage area and as scratch-pad memory, and can also be used tostore input data and processed data. Primary storage can also storeprogramming instructions and data, in the form of data objects and textobjects, in addition to other data and instructions for processesoperating on processor (102). Also as well known in the art, primarystorage typically includes basic operating instructions, program code,data, and objects used by the processor (102) to perform its functions,for example programmed instructions. For example, primary storagedevices (110) can include any suitable computer-readable storage media,described below, depending on whether, for example, data access needs tobe bi-directional or uni-directional. For example, processor (102) canalso directly and very rapidly retrieve and store frequently needed datain a cache memory, not shown. The processor (102) may also include acoprocessor (not shown) as a supplemental processing component to aidthe processor and/or memory (110).

A removable mass storage device (112) provides additional data storagecapacity for the computer system 100, and is coupled eitherbi-directionally (read/write) or uni-directionally (read only) toprocessor (102). For example, storage (112) can also includecomputer-readable media such as flash memory, portable mass storagedevices, holographic storage devices, magnetic devices, magneto-opticaldevices, optical devices, and other storage devices. A fixed massstorage (120) can also, for example, provide additional data storagecapacity. One example of mass storage (120) is an eMMC or microSDdevice. In one embodiment, mass storage (120) is a solid-state driveconnected by a bus (114). Mass storage (112), (120) generally storeadditional programming instructions, data, and the like that typicallyare not in active use by the processor (102). It will be appreciatedthat the information retained within mass storage (112), (120) can beincorporated, if needed, in standard fashion as part of primary storage(110), for example RAM, as virtual memory.

In addition to providing processor (102) access to storage subsystems,bus (114) can be used to provide access to other subsystems and devicesas well. As shown, these can include a display monitor (118), acommunication interface (116), a touch (or physical) keyboard (104), andone or more auxiliary input/output devices (106) including an audiointerface, a sound card, microphone, audio port, audio recording device,audio card, speakers, a touch (or pointing) device, and/or othersubsystems as needed. Besides a touch screen and/or capacitive touchinterface, the auxiliary device (106) can be a mouse, stylus, trackball, or tablet, and is useful for interacting with a graphical userinterface.

The communication interface (116) allows processor (102) to be coupledto another computer, computer network, or telecommunications networkusing a network connection as shown. For example, through thecommunication interface (116), the processor (102) can receiveinformation, for example data objects or program instructions, fromanother network, or output information to another network in the courseof performing method/process steps. Information, often represented as asequence of instructions to be executed on a processor, can be receivedfrom and outputted to another network. An interface card or similardevice and appropriate software implemented by, for exampleexecuted/performed on, processor (102) can be used to connect thecomputer system 100 to an external network and transfer data accordingto standard protocols. For example, various process embodimentsdisclosed herein can be executed on processor (102), or can be performedacross a network such as the Internet, intranet networks, or local areanetworks, in conjunction with a remote processor that shares a portionof the processing. Throughout this specification “network” refers to anyinterconnection between computer components including the Internet,Bluetooth, WiFi, 3G, 4G, 4GLTE, GSM, Ethernet, TCP/IP, intranet,local-area network (“LAN”), home-area network (“HAN”), serialconnection, parallel connection, wide-area network (“WAN”), FibreChannel, PCI/PCI-X, AGP, VLbus, PCI Express, Expresscard, Infiniband,ACCESS.bus, Wireless LAN, HomePNA, Optical Fibre, G.hn, infrarednetwork, satellite network, microwave network, cellular network, virtualprivate network (“VPN”), Universal Serial Bus (“USB”), FireWire, SerialATA, 1-Wire, UNI/O, or any form of connecting homogenous, heterogeneoussystems and/or groups of systems together. Additional mass storagedevices, not shown, can also be connected to processor (102) throughcommunication interface (116).

An auxiliary I/O device interface, not shown, can be used in conjunctionwith computer system 100. The auxiliary I/O device interface can includegeneral and customized interfaces that allow the processor (102) to sendand, more typically, receive data from other devices such asmicrophones, touch-sensitive displays, transducer card readers, tapereaders, voice or handwriting recognizers, biometrics readers, cameras,portable mass storage devices, and other computers.

In addition, various embodiments disclosed herein further relate tocomputer storage products with a computer readable medium that includesprogram code for performing various computer-implemented operations. Thecomputer-readable medium is any data storage device that can store datawhich can thereafter be read by a computer system. Examples ofcomputer-readable media include, but are not limited to, all the mediamentioned above: flash media such as NAND flash, eMMC, SD, compactflash; magnetic media such as hard disks, floppy disks, and magnetictape; optical media such as CD-ROM disks; magneto-optical media such asoptical disks; and specially configured hardware devices such asapplication-specific integrated circuits (“ASIC”s), programmable logicdevices (“PLD”s), and ROM and RAM devices. Examples of program codeinclude both machine code, as produced, for example, by a compiler, orfiles containing higher level code, for example a script, that can beexecuted using an interpreter.

The computer/server system shown in FIG. 1 is but an example of acomputer system suitable for use with the various embodiments disclosedherein. Other computer systems suitable for such use can includeadditional or fewer subsystems. In addition, bus (114) is illustrativeof any interconnection scheme serving to link the subsystems. Othercomputer architectures having different configurations of subsystems mayalso be utilized.

FIG. 2 is an illustration of a generated glitch example. Logic gate(202) is shown here as an illustrative example to be a two-input ANDgate with inputs a and b, output y, and with cell delay of value τ (203)from an input pin a to output pin y. For an input pattern ω₁ (204), theinput for pin a is shown as voltage V(a) (206) as a function of timewith a rising edge, the input for pin b is shown as voltage V(b)(208) asa function of time with a falling edge, and with the interval timebetween the two edges as ζ (210).

A “generated glitch” as referred to herein are glitches generated byfunctional transitions. The two conditions associated with a generatedglitch include that: first, the input pattern is a pattern ω_(k) thatmay cause glitches at output; and second, the interval time ζ betweensuccessive transitions at different inputs is larger than cell delay τ.

As shown in FIG. 2, if ζ<τ (222), no glitch occurs at the output V(y).Alternately, if ζ>τ (224), a glitch occurs at the output of V(y) ofwidth ζ. Thus, as illustrated in FIG. 2 reducing generated glitches maybe addressed by reducing interval time ζ and/or increasing cell delay τby slowing down the cell.

Dual Glitch Power Analysis Engine. A dual glitch power analysis engineis disclosed. A dual glitch power analysis engine calculates accurateglitch power value and incrementally updates design glitch power duringan implementation/P&R flow.

In one embodiment, the dual glitch power analysis engine comprises twoengines which run and have as input a VCD file. One of the two enginesis an annotation engine which extracts information from the VCD file andannotates accurate glitch toggle information from the dynamic simulationthat produced the VCD file. The other of the two engines is astatistical engine which uses boundary pin toggling information.Boundary pins are the input pins and output pins of registers andcomprise a small portion of the design.

The annotation engine is configured to extract the number of annotatedglitches, TG_(anno), for each instance from VCD file and thencalculating an accurate glitch power. The statistical engine estimatesthe number of statistical glitches, TG_(stat), for each instance andglitch power based on the statistical approach for quick glitch poweranalysis. In one embodiment, further enhancements disclosed hereinimprove statistical engine accuracy.

The initial calibration ratios between annotated glitch toggle countsand statistical glitch toggle counts are recorded for each instanceand/or logic gate in the design:

${TG\_ AdjRatio} = \frac{{TG}_{anno}}{{TG}_{stat}}$

After applying this calibration ratio, the initial glitch power from thetwo engines is similar.

During an implementation/P&R glitch power optimization flow, the numberof glitch toggles for each instance may be updated quickly using thestatistical glitch analysis engine. After the update, TG_AdjRatio isapplied to determine a revised glitch number and glitch power in thedesign.

FIG. 3 is an illustration of a glitch bottleneck example. The glitchbottleneck ratio is used to identify glitch bottleneck pins. Determiningglitch power efficiently comprises identifying glitch bottleneck pins.Before identifying glitch bottleneck pins, the bottleneck ratio iscalculated for every pin.

The bottleneck ratio on a leaf output pin, for example pin (302) at theoutput of gate U4 (304), is set as 1. As described in greater detailbelow, the circuit is then backward traversed to calculate thebottleneck ratios for other pins in the fan-in cone, which as shown inFIG. 3 includes pins associated with gate U2 (306) and U1 (308). In oneembodiment, in an analysis engine a circuit is abstracted into adirected graph. The logic gate input/output pins are represented asvertices in the graph. The timing arc from input to output pin in thesame logic gate or the connection from one logic gate output to theother logic gate input are represented as edges in the graph. In orderto traverse the graph, a starting vertex and ending vertex may bedefined. Typical starting vertices include: primary input ports and/orsequential logic gate output pins (e.g. flip-flop Q pins). Typicalending vertices include: primary output ports and/or sequential logicgate inputs (e.g. flip-flop D pins). As described herein, backwardtraversal is traversing this directed graph from ending vertices toinput vertices.

Ratio BN(i) is defined as the bottleneck ratio on pin i. The bottleneckratio of an input pin a is represented as:

${{BN}(a)} = {{P\left( \frac{\partial F}{\partial a} \right)}*{{BN}(o)}}$

where F is the output function of a cell, P(∂F/∂a) is the probability ofthe Boolean difference, and BN(o) is the bottleneck ratio of output pin.The value of the Boolean difference reflects the fact of change of thefunction F with respect to one of its variable a; the Boolean differenceis equal to 1 if such change occurs, and is equal to 0 otherwise.

The bottleneck ratio of an output pin is 1 plus its total bottleneckratio on sink pins. In the example of FIG. 3, the bottleneck ratios forU4_o and U5_o, the output pin of gates U4 (304) and U5 (310)respectively, are 1 since they are the leaf pins in this simple example:

BN(U4_o)=1

BN(U5_o)=1

and the bottleneck ratio of the other output pins for U1_o, U2_o, andU3_o, the output pin of gates U1 (308), U2 (306), and U3 (312)respectively, are:

BN(U1_o)=1+BN(U2_a)+BN(U3_a)

BN(U2_o)=1+BN(U4_a)

BN(U3_o)=1+BN(U5_a)

After all bottleneck ratios are calculated, the critical bottleneck pinsmay be found by multiplying the bottleneck ratio for a given pin withits generated glitch power or propagated glitch power, as described ingreater detail below. The total glitch power propagated to its fanoutPtotal may be represented as follows.

Ptotal(o)=BN(o)*Pglitch(o)

where Pglitch(o) is the glitch power on pin o and BN(o) is thebottleneck ratio on pin o. The most critical bottleneck pin may be foundby sorting Ptotal.

Using a glitch bottleneck is disclosed, for example, for each instanceoutput pin. This results in the total glitch toggles caused by the pinin its fanout cone. With the disclosed dual glitch power analysis,implementation/P&R optimization may focus on high glitch bottleneck pinsto reduce the design glitch power effectively.

FIG. 4 is an illustration of a glitch power bottleneck calculation usinga statistical glitch power analysis. As described above, the pin glitchpower bottleneck ratio of a pin may be expressed as the total glitchtoggle rate at the pin propagated to its fanout. Thus, an approximateglitch power bottleneck is the bottleneck ratio multiplied by the pinglitch power. The bottleneck ratio of the whole design may be calculatedusing a one forward depth-first traverse.

For example, if w_(i) is a glitch toggle propagation rate calculatedduring statistical glitch power analysis, then referring to FIG. 4 thebottleneck ratio for input pin i1 (402) of gate (408) is

BN(i1)=BN(o1)×w ₁

where o1 is the output pin (404) of gate (408), and w₁ is the glitchtoggle propagation ratefor arc it to o1 (406) calculated duringstatistical glitch power analysis. Similarly:

BN(i2)=BN(o2)×w ₂

BN(i3)=BN(o3)×w ₃

BN(i4)=BN(o4)×w ₄

As before, the bottleneck ratio for output pin o3 (422) is equal to 1added to the bottleneck ratio for input pins i1 (402) and i2 (424):

BN(o3)=1+BN(i1)+BN(i2).

Generally,

${R({yi})} = {{P\left( \frac{\partial y}{\partial x_{i}} \right)}{\Pi_{j!=1}\left( {1 - {\beta \times {Tog\_ Rate}\left( x_{j} \right)}} \right)}}$

Here

$\frac{\partial y}{\partial x_{i}}$

term is the Boolean difference of Boolean function y related to it'si-th variable x_(i). The

$P\left( \frac{\partial y}{\partial x_{i}} \right)$

term represents me probability of the Boolean difference to havevalue 1. The Π_(j!=i)(1−β×Tog_Rate(x_(j))) term multiplies all of(1−β×Tog_Rate(x_(j))) together where x_(j) the j-th variable of y and jis any variable in the variable list but i.

FIG. 5 is a flow chart illustrating an embodiment of a process fordynamic power analysis. In one embodiment, the process of FIG. 5 iscarried out by the system of FIG. 1.

In step (502), a logic gate glitch pattern is generated. That is, theinput patterns that may cause a glitch at each logic gate output in thedesign are characterized. For example, a class definition for such aninput pattern may be:

class powGlitchPattern { dbLibPin* _oPin; // output pin with glitchdbLibPin* _leadPin; // Input pin with leading edge dbLibPin* _tailPin;// Input pin with tailing edge bool _leadEdgeFall; // Leading edgepolarity bool _tailEdgeFall; // Tailing edge polarity // 0 Rise 1 FallDdNode* _biasCondition; // Bias condition BoolTree powGlitchPattern*_next; // Next glitch input pattern }; with generation being executedas: foreach inCell arc (input: inP1) that trigger R at outP {  collectinP pointer into array1;  also record inP toggle (R/F) into toggleArr1;} foreach inCell arc (input: inP2) that trigger F at outP {  collectinP2 pointer into array2;  also record inP2 toggle (R/F) intotoggleArr2; } foreach inP1 in arrag1 {  foreach inP2 in arragZ {   if(inP1 == inP2) continue;   // following code need decide which input canbe the leading edge   if (toggleArr1 [inP1] == R && toggleArr2 [inP1] =R) {   // assuming inP1 lead. in temporal domain.   // the input logiccombination for   // inP1/inP is 00, 10, 11   Eval BoolTree of outputpin:    val1 = outP_Bool (inP1 == 0. inP2 == 0) ;   Eval BoolTree ofoutput pin:    val2 = outP_Bool (inP1 == 1. inP2 == 0) ;   Eval BoolTreeof output pin:    val3 = outP_Bool (inP1 == 1. inP2 == 1) ;   if (val1== val3 && val1 != val2) then    inP1 lead, inP2 tail is valid pattern;  // assuming inP1 tail, inP2 lead. then   // the input logic is: 00,01. 11   Eval BoolTree of output pin:    val4 2 outP_Bool (inP1 == 0.inP2 == 1) ;   if (val1 == val3 && val1 != val4) then    inP1 tail, inP2lead is valid pattern;   }   else if (toggleArr1 [inP1] == R &&toggleArr2 [inP1] == F) {    // similar   }   else if (toggleArr1 [inP1]== F & toggleArr2 [inP1] == R) {    // similar   }else { // (toggleArr1[inP1] == F && toggleArr2 [inP1] == F)    // similar    }   } }

The generated logic gate glitch pattern may be used, for example, instep (512) as part of determining generated glitch power.

In step (504), a signal/switching activity report is accessed. In oneembodiment, the signal/switching activity report is imported from a VCDfile. Other data formats and access can be used in other embodiments. Instep (506), the total toggle count (TC) and the number of glitch edges(TG_(anno)) for each instance/gate are extracted from the imported VCDfile:

TC=TC_(func) +TG _(anno)

Wherein TC_(func) is a functional toggle count at each instance. Duringa power optimization flow, this number may be constant.

In step (508), switching activity and signal probability are calculatedbased on information from the signal/switching activity report, forexample a VCD file. In one embodiment, if toggle information is missingin VCD for any instance, the tool propagates switching activity andsignal probability to fill-in missing information. The total togglecount per time duration is its switching activity, wherein the timeduration for each pin when its logic is one is its signal probability.

Expressed in terms of functions, the signal probability P(x) is theaverage fraction of clock cycles in which the steady state value of thenode x is a logic high:

${P(x)} = {\lim\limits_{k\rightarrow\infty}{\frac{1}{k}{\sum\limits_{n = 0}^{k}\;{x\lbrack n\rbrack}}}}$

If a logic signal x(t) makes n_(x)(t) transitions in a time intervals oflength T, then the switching activity of x(t), D(x) is:

${D(x)} = {{\lim\limits_{T\rightarrow\infty}\frac{n_{x}(t)}{T}} = {\lim\limits_{k\rightarrow\infty}{\frac{1}{kT}{\sum\limits_{n = 1}^{k}{{{x\lbrack n\rbrack} - {x\left\lbrack {n - 1} \right\rbrack}}}}}}}$

In terms of switching activity propagation, if the inputs x_(i) to aBoolean module are spatially independent, then the switching activity ofits output y is given by:

$\begin{matrix}{{D(y)} = {\sum\limits_{i = 1}^{n}\;{{P\left( \frac{\partial y}{\partial x_{i}} \right)}{D\left( x_{i} \right)}}}} & (1)\end{matrix}$

In step (510), dynamic power is analyzed. In terms of signal probabilityand switching activity annotation and propagation, the dynamic power isa function of the sum of switching power and internal power.

In order to correlate switching and internal power with signoff results,the signal probability and switching activity may be annotated from theVCD file of step (504). The toggle activity may be extracted, forexample by using a command. As described above, for a net whose signalprobability and switching activity annotation are still missing afterannotation, the AP (the computing engine) may perform signal probabilityand switching activity propagation using the function in equation (1).

In an alternate embodiment, a more sophisticated propagation modelconsidering simultaneous switching is used:

${a(y)} = {{\sum\limits_{i = 1}^{n}{{P\ \left( \frac{\partial y}{\partial x_{i}} \right)}\left( {a\;\left( x_{i} \right)\ {\underset{\underset{1 \leq j \leq n}{j \neq i}}{\;\prod}\left\lbrack {1 - {a\;\left( x_{j} \right)}} \right\rbrack}} \right)}} + {\frac{1}{2}\left\{ {\sum\limits_{1 \leq i < j \leq n}{\left\lbrack {{P\ \left( \frac{{\partial^{2}y}❘_{00}}{{\partial x_{i}}{\partial x_{j}}} \right)} + {P\ \left( \frac{{\partial^{2}y}❘_{01}}{{\partial x_{i}}{\partial x_{j}}} \right)}} \right\rbrack\left( {{a\left( x_{i} \right)}{a\left( x_{j} \right)}{\prod\limits_{l \in {{\{{1,2,\ldots\mspace{14mu},\; n}\}} - {\{{i,j}\}}}}\left\lbrack {1 - {a\left( x_{l} \right)}} \right\rbrack}} \right)}} \right\}}}$

$\frac{{\partial^{2}y}❘_{00}}{{\partial x_{i}}{\partial x_{j}}}\mspace{14mu}{and}\mspace{14mu}\frac{{\partial^{2}y}❘_{01}}{{\partial x_{i}}{\partial x_{j}}}$

wherein a(y) is switching activity on output pin y and are second orderBoolean differences that consider both input x_(i) and x_(j) switchingsimultaneously. The Boolean expression for their calculations are:

$\frac{{\partial^{2}y}❘_{00}}{{\partial x_{i}}{\partial x_{j}}} = {y{_{{x_{i} = 0},{x_{j} = 0}}{\oplus y}}_{{x_{i} = 1},{x_{j} = 1}}}$$\frac{{\partial^{2}y}❘_{01}}{{\partial x_{i}}{\partial x_{j}}} = \left. y \middle| {}_{{x_{i^{=}}0},{x_{j} = 1}}{\oplus y} \right|_{{x_{i} = 1},{x_{j} = 0}}$

P(x) represents the probability of the second order Boolean differencesto have value 1.

As dynamic power is based on the sum of switching power and internalpower, the switching power of one gate i is determined based on

P _(sw) ^(i)=½C _(load) V ² SWA

wherein C_(load) is the gate output loading capacitance; V is the gatesupply voltage; and SW A is the gate output switching activity.

The internal power of one gate i is determined based on

$P_{int}^{i} = {\sum\limits_{j = 1}^{n}{P{W_{table}(j)}{SWA}_{ij}}}$

wherein PW_(table)(j) is an internal power table associated with timingarc from input j to the output of gate i; and SWA_(ij) is part of thegate output switching activity assigned to each input based on equation(1). In the event the same input has multiple conditional arcs, each arcsignal probability also may be applied during this internal powerestimation.

In step (512), a statistical generated glitch power is determined. Inone embodiment, starting from a design boundary like a flip-flop output,primary input (port), and/or gated clock output, a statistical enginecalculates generated glitch toggles at all instance outputs. Thegenerated glitches at an instance output are caused by functionaltoggles at its inputs. The output generated glitch values depend on:

-   -   TC_(func) at its input pins, the leading and tailing pins in the        glitch pattern;    -   cell delay from leading input pin to the output pin;    -   the arrival times at its input pins, the leading and tailing        pins in the glitch pattern; and/or    -   the Boolean function of the logic gate.

A pattern probability is referred to herein as the probability that aninput glitch generating pattern ω_(k) occurs. P_(patt)′(ω_(k)) is theprobability that two inputs toggling together, wherein considering twotoggling inputs has traditionally been accurate enough. P(ω_(i,bias)) isthe probability that non-toggle inputs are biased such that the glitchmay go through the gate:

P _(patt)(ω_(k))=P _(patt)′(ω_(k))·P(ω_(i,bias))

Assuming a glitch input pattern contains two inputs: a and b, then

${P_{patt}^{\prime}\left( \omega_{k} \right)} = {\frac{{D(a)}/2}{f_{clk}} \cdot \frac{{D(b)}/2}{f_{clk}}}$

where D(x) is the switching activity of x, as described above.

Applying Boolean differential operations between the output pin and, forexample, the two input ins in the glitch pattern for simple two-inputgates with output Y and input A, B resolves to

${P\left( \omega_{i,{bias}} \right)} = \frac{\partial^{2}Y}{{\partial A}{\partial B}}$

Taking the example of a 3-input NAND gate where

$\begin{matrix}{{Y = {A \cdot B \cdot C}},\left\{ {\frac{\partial^{2}Y}{{\partial A}{\partial B}} = {{\frac{\partial}{\partial A}\left( \frac{\partial Y}{\partial B} \right)} = {{\frac{\partial}{\partial A}\left\lbrack {\left( {A \cdot 0 \cdot C} \right) \oplus \left( {A \cdot 1 \cdot C} \right)} \right\rbrack} = {{\frac{\partial}{\partial A}\left( {A \cdot C} \right)} = {\left\lbrack {\left( {0 \cdot C} \right) \oplus \left( {1 \cdot C} \right)} \right\rbrack = {\left( {0 \oplus C} \right) = C}}}}}} \right\}} & \;\end{matrix}$

$\frac{\partial^{2}Y}{{\partial A}{\partial B}} = C$

Taking the example of a 3-input NOR gate where Y=A+B+C,

$\frac{\partial^{2}Y}{{\partial A}{\partial B}} = \overset{¯}{C}$

Taking the example of a 3-input XOR gate where Y=A⊕B⊕C,

${\frac{\partial Y}{\partial A} = {\frac{\partial Y}{\partial B} = 1}},{{{force}\mspace{14mu}{P\left( \omega_{i,{bias}} \right)}} = 1}$

Returning to the second condition, that the interval time ζ betweensuccessive transitions at different inputs is larger than cell arcdelay.

As described above, a generated glitch, generated by functionaltransitions, may occur under the condition that the interval time ζbetween successive transitions at different inputs is larger than celldelay τ. The generation probability P_(gen)(ω_(k)) is referred to hereinas the probability that input glitch generating pattern ω_(k) satisfiesthis condition,

P _(gen)(ω_(k))=∫∫_(A) _(k) f(α)f(β)dαdβ

wherein α and β are the arrival times of the respective signals/inputsin ω_(k), f is the distribution function representing the number oftransitions as a function of arrival time, and A_(k) is the area thatsatisfies the condition that the interval time ζ between successivetransitions at different inputs is larger than cell delay τ.

From analysis then, the generated glitch rate, R_(gen)(i) is derived tobe

$\begin{matrix}{{R_{gen}(i)} = {f_{clk}{\sum\limits_{k}\left\{ {{P_{gen}\left( \omega_{k} \right)} \cdot {P_{patt}\left( \omega_{k} \right)}} \right\}}}} & \;\end{matrix}$

wherein f_(clk) is clock frequency.

FIG. 6 is an illustration of a uniform distribution model to determine agenerated glitch rate. Assuming a uniform distribution function in thatthe toggle may evenly occur with arrival window

${f(t)} = {\frac{1}{\left( {\alpha_{\max} - \alpha_{\min}} \right)}\left\{ {{U\left( {t - \alpha_{\min}} \right)} - {U\left( {t - \alpha_{\max}} \right)}} \right\}}$

wherein α_(max) is the latest arrival time and α_(min) is the earliestarrival time (without OCV or On Chip Variation), and U is the stepfunction/uniform distribution function. As shown in FIG. 6, the area“A1”represents the glitch pattern when pin A is leading edge (R), and thearea “A2” represents the glitch pattern when pin B is leading edge (R).

FIG. 7 is an illustration of a uniform distribution model surfaceintegration analysis. As shown in FIG. 7 in various cases, for area A1:

-   -   if β_(max)≤α_(min)+τ_(a), then P_(gen)=0;    -   if β_(min)≤α_(min)+τ_(a), then

${P_{gen} = \frac{{\frac{1}{2}\left\lbrack {\beta_{\max} - \left( {\alpha_{\min} + \tau_{a}} \right)} \right\rbrack}^{2}}{\left( {\beta_{\max} - \beta_{\min}} \right)\left( {\alpha_{\max} - \alpha_{\min}} \right)}};$

-   -   if β_(max)≤α_(max)+τ_(a), then and

${P_{gen} = \frac{\frac{1}{2}\left\lbrack {\beta_{\max} + \beta_{\min} - {2\left( {\alpha_{\min} + \tau_{a}} \right)}} \right\rbrack}{\left( {\alpha_{\max} - \alpha_{\min}} \right)}};$

-   -   if δ_(min)≤α_(max)+τ_(a), then

${P_{gen} = {1 - \frac{{\frac{1}{2}\left\lbrack {\alpha_{\max} + \tau_{a} - \beta_{\min}} \right\rbrack}^{2}}{\left( {\beta_{\max} - \beta_{\min}} \right)\left( {\alpha_{\max} - \alpha_{\min}} \right)}}}.$

As shown in FIG. 7 in various cases, for area A2:

-   -   if β_(max)≤α_(min)+τ_(a), then P_(gen)=0;    -   if β_(max)≤α_(max)+τ_(a), then

${P_{gen} = \frac{{\frac{1}{2}\left\lbrack {\beta_{\max} - \left( {\alpha_{\min} + \tau_{a}} \right)} \right\rbrack}^{2}}{\left( {\beta_{\max} - \beta_{\min}} \right)\left( {\alpha_{\max} - \alpha_{\min}} \right)}};$

-   -   if β_(min)≤α_(min)+τ_(a), then and

${P_{gen} = \frac{\left\lbrack {{2\beta_{\max}} - \left( {\alpha_{\max} + \alpha_{\min} + {2\tau_{a}}} \right)} \right\rbrack}{2\left( {\beta_{\max} - \beta_{\min}} \right)}};$

-   -   if β_(min)≤α_(max)+τ_(a), then

${P_{gen} = {1 - \frac{\left\lbrack {\alpha_{\max} + \tau_{a} - \beta_{\min}} \right\rbrack^{2}}{2\left( {\beta_{\max} - \beta_{\min}} \right)\left( {\alpha_{\max} - \alpha_{\min}} \right)}}}.$

Thus, to reduce the generated glitch rate, R_(gen)(i), two maintechniques are disclosed: gate sizing down in order to increase τ_(a),and adjusting the input arrival time to reduce the generationprobability P_(gen). In summary, using the analysis and assumption of auniform distribution model described in FIG. 6 and FIG. 7, thestatistical generated glitch power may be calculated as described above.

Returning to FIG. 5, in step (514), a statistical propagated glitchpower is determined. The propagated glitches at a logic gate output iscaused by glitch toggles at its input pins. One simplifying modelassumes the inputs of gate have no correlation with each other; and/orthere is sufficient time interval between the input transitions tocalculate a simpler output propagating glitch rate as:

${R(y)} = {\sum\limits_{i = 1}^{n}{{P\left( \frac{\partial y}{\partial x_{i}} \right)}{R\left( x_{i} \right)}}}$

Wherein x_(i) is the i-th input of the gate, y is the output and n isthe total number of inputs.

A more complex model considers that R(x_(i)) is total glitch toggle rateat input x_(i),R(x_(i))=R_(gen)(x_(i))+R_(prop)(x_(i)) but this does notnecessarily consider side input toggle impact to the propagated glitchvalue at output, which may overestimate propagated glitch. As describedherein, “side input” is a related input of a given gate, so that theinputs of the gate are deterministic and/or statistically correlated. Amore accurate algorithm may be developed to calculate propagated glitchthat considers side input toggle impact, such as when multiple sideinputs are switching simultaneously, which reduces the glitchpropagation rate. The above equation

$\begin{matrix}{``{{R(y)} = {\sum\limits_{i = 1}^{n}\;{{P\left( \frac{\partial y}{\partial x_{i}} \right)}{R\left( x_{i} \right)}}}}"} & \;\end{matrix}$

may not consider side input toggling impacts and tends to overestimatepropagated glitch.

Preventing propagated glitch overestimation is disclosed herein bycalculating the propagating glitch rate R_(prop) on output y as

${R_{prop}(y)} = {\sum\limits_{i = 1}^{n}{{P\left( \frac{\partial y}{\partial x_{i}} \right)}{R\left( x_{i} \right)}Bet{a\left( {y,x_{i}} \right)}}}$

wherein

$\left( \frac{\partial y}{\partial x_{i}} \right)$

is the Boolean difference of output y with respect to i_(th) inputx_(i),

$P\left( \frac{\partial y}{\partial x_{i}} \right)$

and is the signal probability of the Boolean difference. R(x_(i)) is theglitch rate of the gate's i_(th) input x_(i),

R(x _(i))=R _(gen)(x _(i))+R _(prop)(x _(i)).

As more side inputs switch, less signal may be propagated. This effectis described herein as a “Side-Input Disturbance (SID)”. As shown below,Beta(y, x_(j)) approximates SID using side-inputs correlatedrelationships, and is useful to determine signal activity on allside-input nodes of a Boolean function.

The computation resource requirement for an exact method usingmultiple-variable Boolean difference probability may grow exponentiallywith the number of independent side-inputs, and so the use of Betareplaces the computationally expensive calculation with multiple-levelcorrelations to increase computation speed/reduce computational powerwithout sacrificing significant accuracy:

${{Beta}\left( {y,x_{i}} \right)} = {{\prod\limits_{j!=i}^{N}\;\left( {1 - {R\left( x_{j} \right)}} \right)} + \frac{\beta_{1}{\sum\limits_{j!=i}^{N}\left( {{R\left( x_{j} \right)}{\sum\limits_{{k!=i},j}^{N}\;\left( {1 - {R\left( x_{k} \right)}} \right)}} \right)}}{N\left( {N - 1} \right)} + \frac{\beta_{2}{\sum\limits_{j!=i}^{N}\;\left( {{R\left( x_{j} \right)}{\prod\limits_{{k!=i},j}^{N}\left( {1 - {R\left( x_{k} \right)}} \right)}} \right)}}{N} - \frac{\beta_{3}{\sum\limits_{j!=i}^{N}\left( {\left( {1 - {R\left( x_{j} \right)}} \right){\prod\limits_{{k!=i},j}^{N}{R\left( x_{k} \right)}}} \right)}}{N}}$

In one embodiment, initially, the weighting coefficients β₁, β₂, and β₃are set to 1.0. A single SID number is calculated using Beta(y, x₁)function and all the subscript j goes from 1 to N including the (j=i=1)variable. The SID number is applied to all arcs (logic arc from inputx_(i) to output y) of the Boolean function as a simplified approximationto calculate the R_(prop)(y). The process stops here if no furtherweighting coefficients fine-tuning is required. The stopping criteriamay be determined by comparing the calculated R_(prop)(y) with goldennumber derived from, for example, VCD input or calculated using theexact method of multiple-variable Boolean difference probability from anumber of digital gates.

If the result does not meet the criteria (e.g. accuracy threshold), thena self-tuned machine learning mechanisms may be applied to derive theweighting coefficients {β1, β3, β3}. In one embodiment, gradientdescent, K-means clustering or simplified convolutionalneural networks(CNN) methods are applied to find a set of best fitted weightingcoefficients for different gate groups, with improved R_(prop) accuracy.Some of the results are served as a testing set. The training, therepeating of deriving and testing, is executed on the fly and stops oncethe stopping criteria can be satisfied. The weighting coefficients foundare applied on the rest of the design. They may be stored in thedatabase to be reused, and as a starting point to train other designs.

The value of Beta(y, x_(j)) of a single input gate (inverter, buffer) is1 since there are no side-inputs to disturb. The Beta(y, x_(j)) of a twoinput gate (2-input NOR, NAND, XOR) can be reduced to (1−R(x_(j))) wherex_(j) is the side-input. The Beta(y, x_(j)) of a three input gate(3-input NOR, NAND, XOR) may be reduced to (1−0.5*(R(x₂)+R(x₃))) wherex₂, x₃ are side-inputs and x₁ is the input calculated on.

The first term “Π_(j!=i) ^(N)(1−R(x_(j)))” in the equation of Beta(y,x_(i)) above represents a first order approximation of SID. The sumapproaches 0 when the side-input pin's toggle rate approaches 1, whichindicates that no glitches may propagate through the gate. When none ofthe side-inputs is toggled (R(x_(j))=0), SID becomes zero.

The second term

$``\frac{\sum\limits_{j!=i}^{N}\left( {{R\left( x_{j} \right)}{\sum\limits_{{k!=i},j}^{N}\left( {1 - {R\left( x_{k} \right)}} \right)}} \right)}{N\left( {N - 1} \right)}"$

in the equation of Beta(y, x_(i)) above represents the correlation ofthe non-toggled side-inputs 1−(1−R(x_(i))) to its neighbors(1−R(x_(k))), one at a time. The sum is normalized.

The third term

$``\frac{\sum\limits_{j!=i}^{N}\left( {{R\left( x_{j} \right)}{\prod\limits_{{k!=i},j}^{N}\left( {1 - {R\left( x_{k} \right)}} \right)}} \right)}{N}"$

in the equation of Beta(y, x_(i)) above represents the correlation ofnon-toggled side-inputs 1−(1−R(x_(j))) to their neighbors (1−R(x_(k))),all at once. The sum is normalized.

$``\frac{\sum\limits_{j!=i}^{N}\left( {\left( {1 - {R\left( x_{j} \right)}} \right){\prod\limits_{{k!=i},j}^{N}{R\left( x_{k} \right)}}} \right)}{N}"$

The fourth term in the equation of Beta(y, x_(i)) above compensates thedouble counting of the multiplication of non-toggled side-inputs. Thesum is normalized. This prevents the R_(prop) from being too pessimisticat the global level but may have a tendency of skewing the resulttowards optimistic on gates with many inputs.

In step (518), a glitch bottleneck ratio is determined. In oneembodiment, the techniques described herein associated with FIG. 3 areused to determine the glitch bottleneck ratio.

In one embodiment, an incremental TC and TG calculation is used. IfTC_(anno) is a total toggle count and TG_(anno) is a total glitch edgesin AP, which are calculated after a file like a VCD file is imported.TC_(anno) and TG_(anno) are updated in an incremental timing updatebased on a TG_(stat) change from the statistical engine:

${TG_{anno}^{new}} = {{\left( \frac{TG_{stat}^{new}}{TG_{stat}^{base}} \right)*TG_{anno}^{base}} = {TG_{stat}^{new} \times TG_{AdjRatio}}}$

wherein TG_(stat) ^(new) is the new statistical glitch count, andTG_(stat) ^(base) is the statistical glitch count before the incrementaloptimization change:

TC_(anno) ^(new)=(TC_(anno) ^(orig) −TG _(anno) ^(orig))+TG _(anno)^(new)

FIG. 8 is a flow chart illustrating an embodiment of a process forglitch power optimization. In one embodiment, the process of FIG. 8 iscarried out by the system of FIG. 1. Optimization is one application ofthe dynamic power analysis of FIG. 5. Reducing the glitch powerconsumption commences with a determination of bottleneck gates throughan MCMM (Multi-Corner Multi-Mode) based bottleneck glitch power analysisquery. For each selected gate, performing optimization techniquesincluding gate sizing and repeater removal to reduce glitch power whilechecking timing and other design QoR (Quality of Results) metrics.

In step (802), a timer performs MCMM timing update and calculates glitchpower of the design. In step (804), an optimizer queries generatedglitch power bottleneck driver pins through a timer's glitch poweranalysis function. In step (806), the optimizer selects gates with anupper bound for power consumption greater than a predetermined thresholdvalue. The optimizer selects optimizable candidate gates to put into agate list and sorts the gate list by timing criticality.

During step (808), for each selected candidate gate the optimizer useshazard filtering and arrival timing balancing techniques to reducegenerated glitch power through the gate. In hazard filtering, gatepropagation delays are adjusted to filter out glitches through the gate.A gate is replaced by a logically equivalent but different sized cell sothat a delay of the gate is changed. The optimizer uses gate upsizingand gate downsizing techniques to balance arrival time through the gate.

In step (810), after optimization for generated glitch power reduction,a timer performs bottleneck-based propagated glitch power analysis. Instep (812), the optimizer selects repeater type gates frombottleneck-based gates with propagated glitch power, and sorts them withtiming criticality. These candidate gates are put into a gate list forpropagated glitch power reduction. In step (814), propagated glitchpower is reduced in part by applying buffer removal and/or inverter pairmerge/removal.

In step (816), in the event glitch power reduction meets a target or theprocess hits the maximum/threshold number of loops, control is ended;otherwise, control is transferred back to step (804) for another loop.

FIG. 9 is an illustration of optimization techniques to reduce generatedglitch power. In the example of FIG. 9, a candidate gate is U3 (902).For generated glitch power reduction, a hazard filtering technique isused to increase the delay of gate U3 (902) to such an extent so thatthe glitch is eliminated and hence generated glitch power on gate U3(902) is eliminated.

The same may be used for generated glitch power reduction, and anarrival timing balancing technique is used for resolving differing pathdelays. Upsizing gate U1 (904) with slower path delay or downsizing gateU2 (906) with faster path delay may reduce glitching transition so thatthe generated glitch power is reduced.

If gate U3 (902) is a buffer type, then it may be removed to eliminateits propagated glitch power if there is no QoR degradation. If gate U3(902) is an inverter type and gate U4 (908) is also an inverter type,then gate U3 (902) and gate U4 (908) may be merged or removed forgenerated power reduction.

FIG. 10A is a flow chart illustrating an embodiment of a process forglitch power analysis. In one embodiment, the process of FIG. 10A iscarried out by the system of FIG. 1.

In step (1002), a switching activity report of simulated switchingactivities of a semiconductor circuit is accessed. For example, aswitching activity report may be and/or includes a VCD file. In oneembodiment, accurate glitch toggle information is determined based atleast in part on the switching activity report. Accurate glitch toggleinformation may be based at least in part on an annotation engineanalysis of the switching activity report.

In one embodiment, boundary pin toggling information is used todetermine statistical glitch toggle information. Boundary pins asdescribed herein are the input pins and output pins of registers. In oneembodiment, statistical glitch toggle information is based at least inpart on a statistical engine estimate such as TG_(stat) described above.In one embodiment, parameters associated with side-input disturbance aredetermined using machine learning. In one embodiment, side-inputdisturbance is accounted for using a first-order approximation, forexample as described above. In one embodiment, side-input disturbance isaccounted for using a correlation of non-toggled side inputs, forexample as described above.

In one embodiment, accurate glitch toggle information is determinedbased on the switching activity report, boundary pin togglinginformation is used to determine statistical glitch toggle information,and a calibration ratio is determined. In one embodiment, a calibrationratio is applied to the statistical glitch toggle value, for example asdescribed above TG_AdjRatio=TG_(anno)/TG_(stat).

In one embodiment, a plurality of glitch counts corresponding to aplurality of gate output pins from the switching activity report areextracted and a plurality of accurate glitch powers based on theplurality of glitch counts determined. In one embodiment, a plurality ofglitch powers corresponding to the plurality of pins is determined.

In one embodiment, updated statistical glitch toggle information isdetermined incrementally based on the adjustment, applying thecalibration ratio to the updated statistical glitch toggle information.

In step (1004), a plurality of glitch bottleneck ratios corresponding toa plurality of pins in the semiconductor circuit is determined, asdescribed in more detail below in FIG. 10B

In step (1006), a plurality of total glitch powers associated with theplurality of pins is determined, wherein each total glitch power of theplurality of total glitch powers being determined based on a glitchbottleneck ratio and a glitch power of a corresponding pin. In oneembodiment, the total glitch power is based at least in part on afunction of the glitch bottleneck ratio multiplied by the glitch powerof the corresponding pin. In one embodiment, the glitch power of thecorresponding pin is based at least in part on one of the following: agenerated glitch power for the corresponding pin and a propagated powerfor the corresponding pin.

In step (1008), one or more critical bottleneck pins among the pluralityof pins is identified based on the plurality of total glitch powers. Inoptional step (1010), one or more gates associated with the one or morecritical bottleneck pins is adjusted to reduce corresponding one or moretotal glitch powers of the one or more gates. In one embodiment, theadjusting of the one or more gates includes one or more of: balancingthe signal toggling time at gate inputs; changing gate delay; making agate delay larger; applying glitch filtering; and/or changing clocklatency of a clock tree. An optimizer such as Aprisa™ by AvatarIntegrated Systems can be used to perform step (1008) and/or step(1010).

FIG. 10B is a flow chart illustrating an embodiment of a process fordetermining glitch bottleneck ratios corresponding to pins. In oneembodiment, the process of FIG. 10B is part of step (1004) in FIG. 10Aand carried out by the system of FIG. 1.

In step (1050) an initial bottleneck ratio is set on a leaf output pin,for example pin (302) at the output of gate U4 (304) in FIG. 3. In step(1052) the semiconductor circuit is backward traversed to determine aplurality of glitch bottleneck ratios of pins in a fan-in cone of theleaf output pin, for example in FIG. 3 the fan-in cone of pin (302)includes pins associated with gate U2 (306) and U1 (308).

A dual glitch power analysis engine has been disclosed. A dual glitchpower analysis engine calculates accurate glitch power value andincrementally updates design glitch power during, for example, animplementation/P&R flow. One of the two engines is an annotation enginewhich extracts information from, for example, a VCD file and annotatesaccurate glitch toggle information from the dynamic simulation thatproduced the file. The other of the two engines is a statistical enginewhich uses boundary pin toggling information. One benefit of thispractical dual glitch power analysis engine is improved analysis andoptimization on reducing dynamic power during circuit design andimplementation. For the target design this may improve battery life,reduce heat and/or thermal noise, improve power efficiency, reduce powerrequirements, and reduce weight/size of a product associated with thetarget design.

Although the foregoing embodiments have been described in some detailfor purposes of clarity of understanding, the invention is not limitedto the details provided. There are many alternative ways of implementingthe invention. The disclosed embodiments are illustrative and notrestrictive.

What is claimed is:
 1. A method, including: accessing a switchingactivity report of simulated switching activities of a semiconductorcircuit; determining a plurality of glitch bottleneck ratioscorresponding to a plurality of pins in the semiconductor circuit,comprising by: setting an initial bottleneck ratio on a leaf output pin;and backward traversing the semiconductor circuit to determine aplurality of glitch bottleneck ratios of pins in a fan-in cone of theleaf output pin; determining a plurality of total glitch powersassociated with the plurality of pins, a total glitch power of theplurality of total glitch powers being determined based on a glitchbottleneck ratio and a glitch power of a corresponding pin; identifyingone or more critical bottleneck pins among the plurality of pins basedon the plurality of total glitch powers; and adjusting one or more gatesassociated with the one or more critical bottleneck pins to reducecorresponding one or more total glitch powers of the one or more gates.2. The method of claim 1, wherein the total glitch power of theplurality of total glitch powers is based at least in part on a functionof the glitch bottleneck ratio multiplied by the glitch power of thecorresponding pin.
 3. The method of claim 1, wherein the glitch power ofthe corresponding pin is based at least in part on a generated glitchpower for the corresponding pin, a propagated power for thecorresponding pin, or both.
 4. The method of claim 1, wherein theswitching activity report includes a VCD (value change dump) file. 5.The method of claim 1, further comprising determining accurate glitchtoggle information based on the switching activity report.
 6. The methodof claim 5, wherein accurate glitch toggle information is based at leastin part on an annotation engine analysis of the switching activityreport.
 7. The method of claim 1, further comprising using boundary pintoggling information to determine statistical glitch toggle information.8. The method of claim 7, wherein statistical glitch toggle informationis based at least in part on a statistical engine estimate.
 9. Themethod of claim 7, wherein the statistical glitch toggle information isdetermined accounting for side-input disturbance.
 10. The method ofclaim 9, wherein parameters associated with side-input disturbance aredetermined using machine learning.
 11. The method of claim 9, whereinside-input disturbance is accounted for using a first-orderapproximation.
 12. The method of claim 9, wherein side-input disturbanceis accounted for using a correlation of non-toggled side inputs.
 13. Themethod of claim 1, further comprising: determining accurate glitchtoggle information based on the switching activity report; usingboundary pin toggling information to determine statistical glitch toggleinformation; and determining a calibration ratio.
 14. The method ofclaim 1, further comprising applying a calibration ratio to astatistical glitch toggle value.
 15. The method of claim 1, furthercomprising extracting a plurality of glitch counts corresponding to aplurality of gate output pins from the switching activity report anddetermining a plurality of accurate glitch powers based on the pluralityof glitch counts.
 16. The method of claim 1, further comprisingdetermining a plurality of glitch powers corresponding to the pluralityof pins.
 17. The method of claim 1, wherein the adjusting of the one ormore gates includes one or more of: balancing a signal toggling time atgate inputs; changing gate delay; making a gate delay larger; applyingglitch filtering; and changing clock latency of a clock tree.
 18. Themethod of claim 1, further comprising determining updated statisticalglitch toggle information incrementally based on the adjustment,applying a calibration ratio to the updated statistical glitch toggleinformation.
 19. A system, comprising: a processor configured to: accessa switching activity report of simulated switching activities of asemiconductor circuit; determine a plurality of glitch bottleneck ratioscorresponding to a plurality of pins in the semiconductor circuit,comprising by: set an initial bottleneck ratio on a leaf output pin;backward traverse the semiconductor circuit to determine a plurality ofglitch bottleneck ratios of pins in a fan-in cone of the leaf outputpin; determine a plurality of total glitch powers associated with theplurality of pins, a total glitch power of the plurality of total glitchpowers being determined based on a glitch bottleneck ratio and a glitchpower of a corresponding pin; identify one or more critical bottleneckpins among the plurality of pins based on the plurality of total glitchpowers; and adjust one or more gates associated with the one or morecritical bottleneck pins to reduce corresponding one or more totalglitch powers of the one or more gates; and a memory coupled to theprocessor and configured to provide the processor with instructions. 20.A computer program product, the computer program product being embodiedin a non-transitory computer readable storage medium and comprisingcomputer instructions for: accessing a switching activity report ofsimulated switching activities of a semiconductor circuit; determining aplurality of glitch bottleneck ratios corresponding to a plurality ofpins in the semiconductor circuit, comprising by: setting an initialbottleneck ratio on a leaf output pin; backward traversing thesemiconductor circuit to determine a plurality of glitch bottleneckratios of pins in a fan-in cone of the leaf output pin; determining aplurality of total glitch powers associated with the plurality of pins,a total glitch power of the plurality of total glitch powers beingdetermined based on a glitch bottleneck ratio and a glitch power of acorresponding pin; identifying one or more critical bottleneck pinsamong the plurality of pins based on the plurality of total glitchpowers; and adjusting one or more gates associated with the one or morecritical bottleneck pins to reduce corresponding one or more totalglitch powers of the one or more gates.